code: def forward(self, x_a, x_b=None):
      if x_b is not None:
          x = torch.cat([x_a, x_b], dim=-1)
      elif isinstance(x_a, list):
          x = sum(x_a)
      else:
          x = x_a
  
      x = self.layer3(x)
      x = self.layer4(x)
      x = x.view(x.size(0), 256 * 4 * 4)
      x = self.classifier(x)
      return x
  
imports: from torch.nn import init
  import sys
  import torch
  import torch
  import torchvision
  from torch import nn
  from datetime import datetime
  from fate.ml.nn.homo.fedavg import FedAVGClient, TrainingArguments
  from fate.ml.nn.model_zoo.agg_layer.fedpass._passport_block import ConvPassportBlock
  from fate.ml.nn.model_zoo.agg_layer.fedpass.agg_layer import FedPassAggLayerHost, FedPassAggLayerGuest
  from tqdm import tqdm
  from fate.ml.nn.model_zoo.hetero_nn_model import HeteroNNModelGuest, HeteroNNModelHost
  from fate.ml.nn.hetero.hetero_nn import HeteroNNTrainerGuest, HeteroNNTrainerHost, TrainingArguments
label_desc: Use image node_id 2 for calling the Alexnet_Top obj's underlying member method code with example usage: obj.forward(x_a, x_b) and returns: x